Learning representations on large-sized graphs is a long-standing challenge due to the inter-
dependence nature involved in massive data points. Transformers, as an emerging class of …

Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …

Graph-structured data exhibits universality and widespread applicability across diverse
domains, such as social network analysis, biochemistry, financial fraud detection, and …

Invariant graph representation learning aims to learn the invariance among data from
different environments for out-of-distribution generalization on graphs. As the graph …

We tackle the problem of graph out-of-distribution (OOD) generalization. Existing graph OOD
algorithms either rely on restricted assumptions or fail to exploit environment information in …

In many real-world applications, graph-structured data used for training and testing have
differences in distribution, such as in high energy physics (HEP) where simulation data used …

Graph neural networks (GNNs) are widely used in machine learning for graph-structured
data. Even though GNNs have achieved remarkable success in real-world applications …

Learning on graphs, where instance nodes are inter-connected, has become one of the
central problems for deep learning, as relational structures are pervasive and induce data …

Combinatorial drug recommendation involves recommending a personalized combination of
medication (drugs) to a patient over his/her longitudinal history, which essentially aims at …

Graph machine learning has been extensively studied in both academia and industry.
Although booming with a vast number of emerging methods and techniques, most of the …